Principal Applied Scientist

Microsoft Audience Network (MSAN) part of the Web Experiences Team  is seeking a Principal Applied Scientist to join us in Redmond, WA or Mountain view, CA. 

This is an opportunity to apply your advanced skills in one of the fastest-growing businesses, to solve real-world problems on a scale. Our goal at MSAN is to maximize value for users and advertisers through our platform.

As the Principal Applied Scientist, you will specialize in creating and enhancing machine learning technologies in areas such as natural language processing(NLP) , computer vision(CV) , and large language models(LLM). You will be a key player within a dynamic team, contributing to and collaborating with other talented colleagues on cutting-edge machine learning challenges.

The Ads Engineering team is at the center stage of this exciting new interdisciplinary field that involves natural language processing, machine learning, data mining, and statistics, to solve difficult problems that arise in online advertising. The central problem of computational advertising is to select an optimized slate of relevant ads for a user to maximize a total utility function that captures the expected revenue, user experience and return on investment for advertisers.

We are a world-class Research and Development (R&D) team of dedicated and talented scientists and engineers who aspire to solve tough problems and turn innovative ideas into high-quality products and services. We help hundreds of millions of users find what they want, and advertisers gain the right audience, thereby directly impacting our business as a Marketplace. 

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond

Our Culture: 

Required qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience

Preferred qualifications:

• PhD with research experience in data science, machine learning and/or related fields
• Experience developing end to end analytics solutions or ML systems for real world applications
• Have publications at peer-reviewed Data Science/AI conferences (e.g. KDD, CIKM, SIGIR, NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)

Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $133,600 - $256,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $173,200 - $282,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Microsoft will accept applications and processes offers for these roles on an ongoing basis.

• Participate in design, implementation, and execution across a variety of features regarding data science and machine learning 
• Finding insights and forming hypothesis on web-scale data with various machine learning, feature engineering, statistical, and data mining techniques: e.g. regression, classification, Natural Language Processing (NLP), optimization, p-values analysis.
• Designing experiments, understanding the resulting data, and producing actionable, trustworthy conclusions from them.
• Crafting and Optimizing Prompts for Effective Large Language Models (LLM) Performance: Design, test, and refine prompts to elicit accurate, relevant, and useful responses from LLMs. This involves understanding the nuances of how the model interprets different inputs, experimenting with various prompt formulations, and iterating based on performance metrics and user feedback.

Other

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